Search Results for "jbpm-start"

Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and...

... are a great start, but it takes more to get to production-ready data validation. Where are Expectations stored? How do they get updated? How do you securely connect to production data systems? How do you notify team members and triage when data validation fails? Great Expectations supports all of these use cases out of the box. Instead of building these components for yourself over weeks or months, you will be able to add production-ready validation to your pipeline in a day.

... the data and start examining it. The next thing you might want to do is get an overview of the kind of data that appears in each column. The stats command will do this for you. The xsv table command takes any CSV data and formats it into aligned columns using elastic tabstops. These commands are instantaneous because they run in time and memory proportional to the size of the slice (which means they will scale to arbitrarily large CSV data).